def main(self):
"""
Method executing the whole pipeline.
"""
##
# get data
##
if (self.dataset in DataLoader.default_datasets
or os.path.exists(self.dataset)):
dataLoader = DataLoader(DataLoader.default_datasets[self.dataset])
data = dataLoader.load()
self.model_params['img_size'] = data.get_dimensions()
self.model_params['label_size'] = data.get_label_dimensions()
else:
print("Dataset " + self.dataset + " does not exist. Aborting...")
return -1
###
# Potential Graph creation
###
if not os.path.exists(os.path.join(self.model_folder, 'model.meta')):
builder = GraphBuilder()
builder.build_graph(self.model_name, self.model_params)
###
# Network training
###
if self.do_training:
network = Network(self.model_name, self.model_folder, self.opt,
self.opt_params, self.num_epochs,
self.batch_size, data, self.summary_folder,
self.summary_intervals, self.complete_set,
self.keep_prob, self.l2_reg, self.clip_gradient,
self.clip_value)
network.load_and_train()
###
# Evaluation
###
if self.do_eval:
evaluator = Evaluation(data, self.model_folder,
self.summary_folder, self.model_name,
self.summary_folder, self.batch_size,
**self.eval_params)
evaluator.evaluate()
print('Finished Evaluation.')
###
# Tensorboard
###
if self.tensorboard and self.do_training:
print("Opening Tensorboard")
os.system("tensorboard --logdir=" + self.summary_folder)
def test_build_graph(self):
#relations = pd.read_csv('..\\..\\..\\Data\\combined_course_structure.csv')
#node_names = list(relations['postreq'])
#print(node_names)
#gb = GraphBuilder(node_names, relations)
#g = gb.build_graph()
#print(g.get_node('Calculus and Analytic Geometry I').get_parents()[0].get_name())
relations = pd.read_csv('..\\..\\..\\Data\\combined_course_structure.csv')
data = pd.read_csv('..\\..\\ExcelFiles\\courses_and_grades.csv')
node_names = list(data.columns)
gb = GraphBuilder(node_names, relations)
g = gb.build_graph()
print(g)
from bayesian_network import BayesianNetwork
from knowledge_base import KnowledgeBase
from graph_builder import GraphBuilder
if __name__ == "__main__":
_data_file_path = '..\\ExcelFiles\\courses_and_grades.csv'
_relations_file_path = '..\\..\\Data\\combined_course_structure.csv'
knowledge_base = KnowledgeBase(_relations_file_path, _data_file_path)
builder = GraphBuilder()
builder = builder.build_nodes(list(knowledge_base.get_data().columns))
builder = builder.add_parents(knowledge_base.get_relations())
builder = builder.add_children()
builder = builder.build_edges()
graph = builder.build_graph()
nodes = graph.get_nodes()
# bayes_net = BayesianNetwork(knowledge_base, graph)
# print(bayes_net.get_graph().get_node('Calculus and Analytic Geometry I').get_parents())
